Training Data With Azure ML

Koos van Strien discusses training data sets and cross-validating results:

When choosing a train and testset, you’ll implicitly introduce a new bias: it could be that the model you just trained predicts well for this particular testset, when trained for this particular trainset. To reduce this bias, you could “cross-validate” your results.

Cross-validation (often abbreviated as just “cv”) splits the dataset into n folds. Each fold is used once as a testset, using all other folds together as a training set. So in our pizza example with 100 records, with 5 folds we will have 5 test runs:

This isn’t Azure ML-specific, and is good reading.

Related Posts

Analyzing Customer Churn With Keras And H2O

Shirin Glander has released code pertaining to a forthcoming book chapter: This is code that accompanies a book chapter on customer churn that I have written for the German dpunkt Verlag. The book is in German and will probably appear in February: https://www.dpunkt.de/buecher/13208/9783864906107-data-science.html.The code you find below can be used to recreate all figures and analyses from this […]

Read More

Tips On Running SQL Server In RDS

Matthew McGiffen shares some tips on running SQL Server in Amazon RDS: Or you can go with Amazon RDS (Relational Database Service).  This is more of a managed service where Amazon looks after some aspects of your database server for you. In return you give up some of the control you would have with your […]

Read More

Categories

August 2016
MTWTFSS
« Jul Sep »
1234567
891011121314
15161718192021
22232425262728
293031